Systems and Methods for Monitoring and Maintaining Stability of Vehicle Cargo

ABSTRACT

A system includes a plurality of sensors arranged in a vehicle to monitor an item in a storage area of a vehicle. The system comprises a data processing module configured to process data from the sensors, determine whether the item in the storage area of the vehicle is likely to move within the storage area or is likely to fall from the vehicle during travel, and generate a first indication that the item is likely to move within the storage area or is likely to fall from the vehicle during travel. The data processing module is configured to determine whether the item has moved within the storage area or has fallen from the vehicle during travel and generate a second indication that the item has moved within the storage area or has fallen from the vehicle during travel.

INTRODUCTION

The information provided in this section is for the purpose of generallypresenting the context of the disclosure. Work of the presently namedinventors, to the extent it is described in this section, as well asaspects of the description that may not otherwise qualify as prior artat the time of filing, are neither expressly nor impliedly admitted asprior art against the present disclosure.

The present disclosure relates to systems and methods for monitoring andmaintaining stability of vehicle cargo.

Shifting cargo in vehicles (e.g., in cargo beds, trunks, roof mountedracks, trailers, and so on) can distract drivers. Further, items thatfall from these cargo areas of the vehicles can litter roadways andhinder drivability of other vehicles on the roadways.

SUMMARY

A system comprises a plurality of sensors arranged in a vehicle tomonitor an item in a storage area of a vehicle. The system comprises adata processing module configured to process data from the sensors,determine whether the item in the storage area of the vehicle is likelyto move within the storage area or is likely to fall from the vehicleduring travel, and generate a first indication that the item is likelyto move within the storage area or is likely to fall from the vehicleduring travel. The data processing module is configured to determinewhether the item has moved within the storage area or has fallen fromthe vehicle during travel and generate a second indication that the itemhas moved within the storage area or has fallen from the vehicle duringtravel.

In another feature, the first indication includes a suggestion regardingone or more of repositioning the item in the storage area, a route to alocation to pull over for repositioning the item in the storage area, analternate route for the travel, and a change in driving behavior.

In another feature, the data processing module is further configured toinstruct a suspension subsystem to adjust suspension of the vehicle inresponse to determining that the item is likely to move within thestorage area or is likely to fall from the vehicle during travel.

In another feature, the second indication includes an identification ofthe item, a location where the item fell from the vehicle, and a routeto the location in response to determining that the item fell from thevehicle during travel.

In another feature, the data processing module is further configured togenerate a message to send to another vehicle including a location wherethe item fell from the vehicle in response to determining that the itemfell from the vehicle during travel.

In another feature, the data processing module is further configured toverify, before generating the first indication, whether the item islikely to move within the storage area or is likely to fall from thevehicle during travel using a combination of data received from two ormore of the sensors.

In another feature, the data processing module is further configured toverify, before generating the second indication, whether the item hasmoved within the storage area or has fallen from the vehicle using acombination of data received from two or more of the sensors.

In another feature, the sensors include two or more of a camera, aweight sensor, a suspension sensor, a radar sensor, an ultrasonicsensor, and a tire pressure sensor.

In another feature, the storage area includes at least one of a trunk ofthe vehicle, a cargo area of the vehicle, a rack mounted on a roof ofthe vehicle, and a trailer hitched to the vehicle.

In another feature, the data processing module comprises a neuralnetwork to generate the first indication. The neural network is trainedusing machine learning to determine when the item is likely to movewithin the storage area or is likely to fall from the vehicle duringtravel based on the data from the sensors and additional data regardingat least one of road design, road conditions, and speed limit of a routeselected for travel, and historical driving behavior of a driver of thevehicle.

In still other features, a system comprises a processor and a memorycomprising instructions for execution by the processor to process datafrom a plurality of sensors arranged in a vehicle to monitor an item ina storage area of a vehicle. The instructions further cause theprocessor to determine whether the item in the storage area of thevehicle is likely to move within the storage area or is likely to fallfrom the vehicle during travel, and generate a first indication that theitem is likely to move within the storage area or is likely to fall fromthe vehicle during travel. The instructions further cause the processorto determine whether the item has moved within the storage area or hasfallen from the vehicle during travel, and generate a second indicationthat the item has moved within the storage area or has fallen from thevehicle during travel.

In another feature, the instructions further cause the processor toinclude in the first indication a suggestion regarding one or more ofrepositioning the item in the storage area, a route to a location topull over for repositioning the item in the storage area, an alternateroute for the travel, and a change in driving behavior.

In another feature, the instructions further cause the processor toadjust suspension of the vehicle in response to determining that theitem is likely to move within the storage area or is likely to fall fromthe vehicle during travel.

In another feature, the instructions further cause the processor toinclude in the second indication an identification of the item, alocation where the item fell from the vehicle, and a route to thelocation in response to determining that the item fell from the vehicleduring travel.

In another feature, the instructions further cause the processor togenerate a message to send to another vehicle including a location wherethe item fell from the vehicle in response to determining that the itemfell from the vehicle during travel.

In another feature, the instructions further cause the processor toverify, before generating the first indication, whether the item islikely to move within the storage area or is likely to fall from thevehicle during travel using a combination of data received from two ormore of the sensors.

In another feature, the instructions further cause the processor toverify, before generating the second indication, whether the item hasmoved within the storage area or has fallen from the vehicle using acombination of data received from two or more of the sensors.

In another feature, the sensors include two or more of a camera, aweight sensor, a suspension sensor, a radar sensor, an ultrasonicsensor, and a tire pressure sensor.

In another feature, the storage area includes at least one of a trunk ofthe vehicle, a cargo area of the vehicle, a rack mounted on a roof ofthe vehicle, and a trailer hitched to the vehicle.

In another feature, the system further comprising a neural networkconfigured to generate the first indication. The neural network istrained using machine learning to determine when the item is likely tomove within the storage area or is likely to fall from the vehicleduring travel based on the data from the sensors and additional dataregarding at least one of road design, road conditions, and speed limitof a route selected for travel, and historical driving behavior of adriver of the vehicle.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description, the claims and the drawings. Thedetailed description and specific examples are intended for purposes ofillustration only and are not intended to limit the scope of thedisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description and the accompanying drawings, wherein:

FIG. 1 shows an example of items in a storage area 100 associated with avehicle;

FIG. 2 shows a system for monitoring and maintaining stability ofvehicle cargo according to the present disclosure; and

FIG. 3 shows a method for monitoring and maintaining stability ofvehicle cargo according to the present disclosure.

In the drawings, reference numbers may be reused to identify similarand/or identical elements.

DETAILED DESCRIPTION

When hauling items in a truck's cargo bed or in other storage areas of avehicle or trailer, drivers are often concerned that items may shift orfly-out, causing damage to the cargo as well as posing a potentialhazard for other vehicles on the road. To address this issue, camerascan be aimed at the cargo bed to allow drivers to visually examine thecargo bed. However, such systems provide only a view of the cargo bedthat requires the driver to continually monitor the display for changesin the cargo's state. The present disclosure aims to reduce the burdenon the driver by providing automatic warnings when cargo items shift orfly-out, which eliminates the need to continually monitor the cargo.

The present disclosure provides a system for automatically detectingwhether items in a cargo bed or storage area of a vehicle shift orfly-out. The system uses an array of sensors such as but not limited tocameras, ultrasonic sensors, suspension sensors, tire pressure sensors,weight sensors, and radar sensors to monitor activity (i.e., movement)of items. If a shift or fly-out event is detected, the driver isnotified/alerted with a relevant human machine interface (HMI), such asan augmented reality camera view, in the vehicle's center-stack displayand/or gauge cluster. If a fly-out of a large object is detected and itslocation may pose a threat to other traffic, other vehicles can benotified using a vehicle-to-vehicle (V2V) network or other (e.g.,subscription based) communication systems. Automatic suspensionadjustment can also be made if the vehicle becomes unbalanced due tocargo shift. The system relies on artificial intelligence to make thesedeterminations. A machine learning database is used to predict/preventcargo shift/fly-out event by using current and historical data combinedwith knowledge of road design, road conditions, speed limits, andpersonal driving habits. The system can be used with various cargo areasof and associated with vehicles such as but not limited to trunks,rooftops, flat beds, open/closed towed trailers, etc.

For example, the system provides an automatic pop-up video when items incargo area have shifted, flown-out, or have changed in appearance,position, and/or state. The system automatically saves a video file oftime period before cargo items are shifted/flown-out, which can be laterreviewed. The system visually highlights loose, at risk, or shifteditems with an appropriate HMI notification and an augmented reality livevideo describing changes in and current status of cargo. The systemautomatically adjusts the suspension if the vehicle becomes unbalanceddue to the cargo shift.

The system provides automatic advice on what to do on detectingloose/shifted items. For example, the advice can include but is notlimited to navigation to a stop (pull over) location, navigation pins ona map indicating locations of fly-out items and route to return tofly-out location, alerting relevant authorities of road hazard,automatic video pop-up display with alert, and displaying a cargo stateicon (e.g., Good, Recent Weight Shift, Fly-out, ConsiderReducing/Repositioning Cargo), and so on.

The system provides automatic advice on what to do to preventloose/shifted items. For example, the advice can include but is notlimited to re-routing to destination via routes with better road design(e.g., less curves, traffic circles, etc.), better road conditions(smoother, no potholes/construction/dirt), providing real-timerecommendations for driving behavior and vehicle speed for upcoming roadconditions and road designs, and so on. The system notifies additionalparties such as police and other vehicles via V2V network or othercommunication systems when a large flown out object poses a threat toother traffic.

The system reduces the need to manually monitor the cargo by turningbody and head or looking at a camera view by taking eyes off the roadwhile driving. Instead, the system automatically provides relevantinformation including preventive and post cargo event advice to thedrivers when a cargo event such as a shift or a fly-out occurs, or toprevent an event from occurring. The system predicts cargo shifts andprovides advice on route and driver behavior to prevent cargoshifts/fly-outs. Thus, the system relieves the drivers from the cargomonitoring task and frees up the drivers to maintain eyes on the roadwhile driving.

The system utilizes augmented reality, computer vision, featuretracking, cameras, ultrasonic sensors, weight sensors, radar sensors,cloud database, and artificial intelligence to automatically andcontinuously monitor and detect whether cargo in vehicle stowage areahas shifted, changed state, or flew out of vehicle. The system employsthe following methodology, which is described below in detail, to detectif cargo has shifted or flown-out of vehicle: The system predicts andidentifies at-risk items for shift/fly-out based on micro-movementsdetected by cameras and weight sensors combined with knowledge of roaddesign, road conditions, and speed limit on route as well as historicaldriver behavior. The system provides automated notifications/alerts andfeedback including augmented reality live view video images to informthe driver of movement and path of shifting cargo or fly-out. The systemprovides location and other alerting information to host vehicle driverand other vehicle driver in case of cargo fly-out. The system providesreal-time navigational (route) and driving performance advice(acceleration/braking/turning behavior) to prevent cargo shift/fly-out.The system performs automatic suspension adjustment to rebalance thevehicle if necessary. The system utilizes machine learning database todetermine if shifting cargo presents an added risk of damage or fly-out;and so on. These and other features of the system of the presentdisclosure are now described below in further detail.

FIG. 1 shows an example of items in a storage area 100 associated with avehicle. For example, the storage area 100 may include but is notlimited to a trunk of a car, cargo area of a van or a truck, a flat bedof a pickup truck, a rack mounted on a roof of a vehicle, a trailerhitched to a vehicle, and so on. In the example shown, Item 1 102 isshown as having fallen from the vehicle, Item 2 104 is shown as havingshifted position in a direction indicated by an arrow, and Item 3 106 isshown as being stable (i.e., not moving around in the storage area 100).

FIG. 2 shows a system 200 for monitoring and maintaining stability ofvehicle cargo in the vehicle's storage area according to the presentdisclosure. The system 200 comprises a vehicle 202, a storage area 204of the vehicle 202, a plurality of sensors 206, a data processing module208, an infotainment subsystem 210, other vehicle subsystems 212, and acommunication module 214.

For example, the storage area 204 of the vehicle 202 may be similar tothe storage area 100 shown and described above with reference to FIG. 1.The sensors 206 can include but are not limited to cameras, ultrasonicsensors (used for parking), suspension sensors, tire pressure sensors,weight sensors, radar sensors, and so on. The sensors 206 may bedistributed throughout the vehicle 202. Based on the data collected fromthe sensors 206, the infotainment subsystem 210 can provide audiovisualindications to the driver of the vehicle 202 about the state of thecargo in the storage area 204 of the vehicle 202.

The other vehicle subsystems 212 can include but are not limited to anavigation subsystem, a suspension subsystem, a braking subsystem, atraction subsystem, an autonomous driving subsystem, and so on. Forexample, the navigation subsystem can include a GPS subsystem, and atraffic and weather subsystem. Each subsystem including elements 210 and212 include a controller that controls the respective subsystem viarespective actuators.

The communication module 214 can communicate with other vehicles 218 anda server 220 (e.g., in a cloud) via a communication system 216 (e.g., acellular network, a vehicle-to-vehicle or V2V network, avehicle-to-infrastructure or V2I network, and so on).

Each sensor 206 can detect a different aspect or characteristic of theitems in the storage area 204 of the vehicle 202. For example, a weightsensor can sense weight of an item or a change in weight in the storagearea 204 due to movement (or fly-out) of an item. A camera and a radarsensor can detect movement of fly-out of an item; and so on.Accordingly, the sensors 206 can collect various types of data that canbe used in combination to determine position and movement of items inthe storage area 204 of the vehicle 202. The sensors 206 output the datato the data processing module 208. The data processing module 208analyzes the data from a combination of the sensors 206. Based on theanalysis, the data processing module 208 determines the position andmovement of the items in the storage area 204 of the vehicle 202.

Some of the sensors 206 also output data to corresponding vehiclesubsystems 212. For example, the weight sensors provide data to asuspension subsystem, which can adjust the suspension of the vehicle 202(e.g., by using a damping system, a load leveling system, or both). Adamping system adjusts actuators in dampers based on vehicle speed,acceleration, and steering angle to reduce vibration and improve rideexperience. A load leveling system maintains ride height of the vehicleabove the road regardless of the load (i.e., weight of the items) in thevehicle. In other examples, the tire pressure sensors provide data totraction control subsystem. Ultrasonic sensors provide data to a parkingsubsystem. Radar sensors provide data to an obstacle detectionsubsystem, a lane control subsystem, an autonomous driving subsystem,and so on. These other vehicle subsystems 212 in turn can provide datato and receive data from the data processing module 208 for monitoringand maintaining stability of vehicle cargo.

The data processing module 208 outputs various alerts (e.g.,indications) for the driver of the vehicle 202 via the infotainmentsubsystem 210. The alerts can be messages, signs such as icons, audioand/or visual indications, videos showing item movement of fly-out, andso on. The alerts indicate status of items (e.g., shifting, flying out)in the storage area 204 of the vehicle 202.

For example, when the driver loads the items in the storage area 204 ofthe vehicle 202, the data processing module 208 can suggest to thedriver via the infotainment subsystem 210 that some of the items mayneed to be rearranged or redistributed or else the items can move aboutin the storage area 204 or can fly out of the vehicle 202 during travel.If the driver does not follow the suggestions, the data processingmodule 208 may further advise the driver to take a particular route tothe destination, assuming that the driver inputs the destination into anavigation subsystem of the vehicle 202. For example, the suggestedroute may include fewer curves, turns, traffic circles, speed bumps,construction and so on, which can minimize the risk of the itemsshifting and/or flying out of the vehicle 202. For example, the dataprocessing module 208 can obtain such information from the navigationsubsystem onboard the vehicle 202.

In addition, the data processing module 208 can suggest to the drivervia the infotainment subsystem 210 that the driver should drive at aparticular speed, not accelerate/decelerate rapidly, not brake abruptly,make smooth turns, and so on (collectively called driving behavior), soas to minimize the risk of the items shifting and/or flying out of thevehicle 202. Additionally, the data processing module 208 can instructthe suspension subsystem to automatically adjust the suspension of thevehicle 202 (e.g., by using a damping system and/or a load levelingsystem) such that the vehicle 202 is rebalanced, which can minimize therisk of the items shifting and/or flying out of the vehicle 202.

If an item shifts during the travel, the data processing module 208detects that based on the data received from the sensors 206. Forexample, the data processing module 208 can predict and identify at-riskitems for shift/fly-out based on micro-movements of items detected bycameras and weight sensors. The data processing module 208 can furtherbase the prediction based on data stored on and obtained from the server220 regarding road design, road conditions, and speed limit on the routebeing traveled as well as the driver's historical driving behavior. Thedata processing module 208 may generate an alert for the driver that isoutput via the infotainment subsystem 210, which can include a videoshowing the item shifting. The data processing module 208 canadditionally locate (using the onboard navigation system) and output viathe infotainment subsystem 210 a location of an upcoming parking spotwhere the vehicle can be pulled over to reposition the items in thestorage area 204. In addition, the data processing module 208 can againadvise the driver regarding an alternate route, altering drivingbehavior, and so on as described above. The data processing module 208can further readjust the suspension after the driver has repositionedthe items in the storage area 204.

If an item falls out of the vehicle 202, the data processing module 208detects that based on the data received from the sensors 206. The dataprocessing module 208 may generate an alert for the driver of thevehicle 202 that is output via the infotainment subsystem 210, which caninclude a video showing the item flying out of the vehicle 202. Inaddition, using the onboard navigation system, the data processingmodule 208 can identify the location where the item flew out of thevehicle and indicate the location on the map on the infotainmentsubsystem 210. Further, using the onboard navigation system, the dataprocessing module 208 can also provide a route to the location where theitem fell from the vehicle 202 so that the driver can quickly locate andretrieve item. The video showing the item flying out of the vehicle 202can indicate to the driver the surroundings in which the item flew out,which can further assist the driver in locating and retrieving the item.

In addition, upon detecting a fly-out event, the data processing module208 can generate a message that the communication module 214 cantransmit to police and other vehicles (e.g., within a particular rangeof the vehicle 202) via the communication system 216 (e.g., a cellularnetwork, a V2V network, a V2I network, etc.). For example, the messagecan include a possible description of the item (e.g., bulky, weight,hazardous, etc.) and the location where the item fell from the vehicle202. This can help police and other vehicles on the road to locate theitem and avoid or minimize any hazard posed by the item.

Often, input from just one senor may be insufficient to determinewhether an item has moved or flown out of the vehicle 202. Rather,inputs from multiple different sensors may be used to make thedetermination. Further, the determination that an item has moved orflown out of the vehicle 202 can be verified or confirmed by usinginputs from other additional sensors such as weight sensors andultrasonic sensors. Radar sensors located in rear corners and rearcenter of the vehicle can be particularly helpful in detecting itemsflying out of the vehicle 202.

False positives can also be identified using data from multiple sensors.For example, consider a vehicle carrying liquid in a tank. The liquidoften sways in the tank. While the weight sensors and suspension sensorswill detect a shifting item, a camera can observe that the tank isstable although the liquid in the tank is swaying. Thus, based on thecombined inputs from all these sensors, the data processing module 208can resolve the ambiguous or apparent shifting in this example and willnot indicate a shifting item.

The server 220 in the cloud stores various types of data including butnot limited to drivers' driving behavior, road designs, road conditions,and so on. Additionally, the server 220 stores data from variousvehicles regarding cargo shifts and fly-out locations, including dataabout the items. The data processing module 208 accesses this databasein the server 220 via the communication module 214 and the communicationsystem 216. The data processing module 208 uses machine learningtechniques and uses this data from the server 220 to train a neuralnetwork to identify risk of cargo shift and fly-out and to determine ifa shifting cargo presents an added risk of fly-out. After making such adetermination, the data processing module 208 suggests to the driver ofthe vehicle 202 via the infotainment subsystem 210 how and where toreposition the at-risk item in the storage area 204, an alternate routeto travel to prevent shifting or flying out, and changes in drivingbehavior as explained above.

For example, a neural network can be trained using data collected by theserver 220 from vehicles about shifts and fly-outs occurring inparticular geographic areas. For example, the data can include actualcargo events such as shifts or fly-outs (e.g., confirmed by drivers) ora relatively large lateral or longitudinal force detected by vehicles'sensors. The trained neural network can then be deployed and used withsome confidence by the data processing module 208 to predict shifts andfly-outs in these and other geographic areas having similar roadconditions, for example. The neural network can be continually trainedbased on new data of cargo events collected by the server 220 on anongoing basis to further improve the predicting capability of the neuralnetwork. Thus, the server 220 not only provides historical data but alsofunctions as another data processor in the cloud in addition to the dataprocessing module 208 in the vehicle 202.

FIG. 3 shows a method 300 for monitoring and maintaining stability ofvehicle cargo according to the present disclosure. For example, themethod 300 may be performed by the system 200. For example, one or moreelements of the system 200, such as but not limited to the dataprocessing module 208, may perform one or more steps of the method 300.

At 302, the method 300 monitors items in a storage area of a vehicle(e.g., storage area 204) using various sensors (e.g., sensors 206). At304, the method 300 determines based on the data received from thesensors whether an item is likely to move or fly out of the vehicle. Themethod 300 uses a trained neural network to predict whether an item islikely to move or fly out of the vehicle. The method 300 returns to 302if the items in the storage area are not likely to move or fly out. Themethod 300 proceeds to 306 if an item is likely to move or fly out ofthe vehicle.

At 306, the method 300 alerts the driver of the vehicle that an item islikely to move or fly out of the vehicle. The method 300 provides thedriver with suggestions regarding repositioning the item that is likelyto move or fly out of the vehicle. The method 300 provides the driver analternate route to travel to prevent or minimize the chances of the itemmoving or flying out of the vehicle. The method 300 adjusts thesuspension of the vehicle to rebalance the load in the vehicle.

At 308, the method 300 determines if an item has moved. The method 300returns to 302 if an item has not moved. The method 300 proceeds to 310if an item has moved. At 310, the method 300 verifies or confirms usingsensor data (e.g., ultrasonic and weight sensor data) that the item hasin fact moved. The method 300 alerts the driver of the vehicle that anitem has moved. The method 300 provides the driver with suggestionsregarding repositioning the item. The method 300 provides the driver alocation of an upcoming or nearest parking spot where the driver canpull over and reposition the item. The method 300 provides the driver analternate route to travel to prevent or minimize the chances of the itemmoving or flying out of the vehicle. The method 300 adjusts thesuspension of the vehicle to rebalance the load in the vehicle. Themethod 300 returns to 302.

At 312, the method 300 determines if an item has flown out of thevehicle. The method 300 returns to 302 if an item has not flown out ofthe vehicle. The method 300 proceeds to 314 if an item has flown out ofthe vehicle. At 314, the method 300 verifies or confirms using sensordata (e.g., radar and weight sensor data) that the item has in factflown out of the vehicle. The method 300 alerts the driver of thevehicle that an item has flown out of the vehicle. The method 300 alertsother parties such as police and other vehicles in the vicinity that anitem has flown out on the roadway. The method 300 provides the driverwith a video recording showing the item flying out of the vehicle alongwith a location and route to retrieve the item. The method 300 returnsto 302.

The foregoing description is merely illustrative in nature and is notintended to limit the disclosure, its application, or uses. The broadteachings of the disclosure can be implemented in a variety of forms.Therefore, while this disclosure includes particular examples, the truescope of the disclosure should not be so limited since othermodifications will become apparent upon a study of the drawings, thespecification, and the following claims. It should be understood thatone or more steps within a method may be executed in different order (orconcurrently) without altering the principles of the present disclosure.Further, although each of the embodiments is described above as havingcertain features, any one or more of those features described withrespect to any embodiment of the disclosure can be implemented in and/orcombined with features of any of the other embodiments, even if thatcombination is not explicitly described. In other words, the describedembodiments are not mutually exclusive, and permutations of one or moreembodiments with one another remain within the scope of this disclosure.

Spatial and functional relationships between elements (for example,between modules, circuit elements, semiconductor layers, etc.) aredescribed using various terms, including “connected,” “engaged,”“coupled,” “adjacent,” “next to,” “on top of,” “above,” “below,” and“disposed.” Unless explicitly described as being “direct,” when arelationship between first and second elements is described in the abovedisclosure, that relationship can be a direct relationship where noother intervening elements are present between the first and secondelements, but can also be an indirect relationship where one or moreintervening elements are present (either spatially or functionally)between the first and second elements. As used herein, the phrase atleast one of A, B, and C should be construed to mean a logical (A OR BOR C), using a non-exclusive logical OR, and should not be construed tomean “at least one of A, at least one of B, and at least one of C.”

In the figures, the direction of an arrow, as indicated by thearrowhead, generally demonstrates the flow of information (such as dataor instructions) that is of interest to the illustration. For example,when element A and element B exchange a variety of information butinformation transmitted from element A to element B is relevant to theillustration, the arrow may point from element A to element B. Thisunidirectional arrow does not imply that no other information istransmitted from element B to element A. Further, for information sentfrom element A to element B, element B may send requests for, or receiptacknowledgements of, the information to element A.

In this application, including the definitions below, the term “module”or the term “controller” may be replaced with the term “circuit.” Theterm “module” may refer to, be part of, or include: an ApplicationSpecific Integrated Circuit (ASIC); a digital, analog, or mixedanalog/digital discrete circuit; a digital, analog, or mixedanalog/digital integrated circuit; a combinational logic circuit; afield programmable gate array (FPGA); a processor circuit (shared,dedicated, or group) that executes code; a memory circuit (shared,dedicated, or group) that stores code executed by the processor circuit;other suitable hardware components that provide the describedfunctionality; or a combination of some or all of the above, such as ina system-on-chip.

The module may include one or more interface circuits. In some examples,the interface circuits may include wired or wireless interfaces that areconnected to a local area network (LAN), the Internet, a wide areanetwork (WAN), or combinations thereof. The functionality of any givenmodule of the present disclosure may be distributed among multiplemodules that are connected via interface circuits. For example, multiplemodules may allow load balancing. In a further example, a server (alsoknown as remote, or cloud) module may accomplish some functionality onbehalf of a client module.

The term code, as used above, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes, datastructures, and/or objects. The term shared processor circuitencompasses a single processor circuit that executes some or all codefrom multiple modules. The term group processor circuit encompasses aprocessor circuit that, in combination with additional processorcircuits, executes some or all code from one or more modules. Referencesto multiple processor circuits encompass multiple processor circuits ondiscrete dies, multiple processor circuits on a single die, multiplecores of a single processor circuit, multiple threads of a singleprocessor circuit, or a combination of the above. The term shared memorycircuit encompasses a single memory circuit that stores some or all codefrom multiple modules. The term group memory circuit encompasses amemory circuit that, in combination with additional memories, storessome or all code from one or more modules.

The term memory circuit is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave); the term computer-readablemedium may therefore be considered tangible and non-transitory.Non-limiting examples of a non-transitory, tangible computer-readablemedium are nonvolatile memory circuits (such as a flash memory circuit,an erasable programmable read-only memory circuit, or a mask read-onlymemory circuit), volatile memory circuits (such as a static randomaccess memory circuit or a dynamic random access memory circuit),magnetic storage media (such as an analog or digital magnetic tape or ahard disk drive), and optical storage media (such as a CD, a DVD, or aBlu-ray Disc).

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks,flowchart components, and other elements described above serve assoftware specifications, which can be translated into the computerprograms by the routine work of a skilled technician or programmer.

The computer programs include processor-executable instructions that arestored on at least one non-transitory, tangible computer-readablemedium. The computer programs may also include or rely on stored data.The computer programs may encompass a basic input/output system (BIOS)that interacts with hardware of the special purpose computer, devicedrivers that interact with particular devices of the special purposecomputer, one or more operating systems, user applications, backgroundservices, background applications, etc.

The computer programs may include: (i) descriptive text to be parsed,such as HTML (hypertext markup language), XML (extensible markuplanguage), or JSON (JavaScript Object Notation) (ii) assembly code,(iii) object code generated from source code by a compiler, (iv) sourcecode for execution by an interpreter, (v) source code for compilationand execution by a just-in-time compiler, etc. As examples only, sourcecode may be written using syntax from languages including C, C++, C #,Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl,Pascal, Curl, OCaml, Javascript®, HTML5 (Hypertext Markup Language 5threvision), Ada, ASP (Active Server Pages), PHP (PHP: HypertextPreprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, VisualBasic®, Lua, MATLAB, SIMULINK, and Python®.

What is claimed is:
 1. A system comprising: a plurality of sensors arranged in a vehicle to monitor an item in a storage area of a vehicle; and a data processing module configured to: process data from the sensors; determine whether the item in the storage area of the vehicle is likely to move within the storage area or is likely to fall from the vehicle during travel; generate a first indication that the item is likely to move within the storage area or is likely to fall from the vehicle during travel; determine whether the item has moved within the storage area or has fallen from the vehicle during travel; and generate a second indication that the item has moved within the storage area or has fallen from the vehicle during travel.
 2. The system of claim 1 wherein the first indication includes a suggestion regarding one or more of repositioning the item in the storage area, a route to a location to pull over for repositioning the item in the storage area, an alternate route for the travel, and a change in driving behavior.
 3. The system of claim 1 wherein the data processing module is further configured to instruct a suspension subsystem to adjust suspension of the vehicle in response to determining that the item is likely to move within the storage area or is likely to fall from the vehicle during travel.
 4. The system of claim 1 wherein the second indication includes an identification of the item, a location where the item fell from the vehicle, and a route to the location in response to determining that the item fell from the vehicle during travel.
 5. The system of claim 1 wherein the data processing module is further configured to generate a message to send to another vehicle including a location where the item fell from the vehicle in response to determining that the item fell from the vehicle during travel.
 6. The system of claim 1 wherein the data processing module is further configured to verify, before generating the first indication, whether the item is likely to move within the storage area or is likely to fall from the vehicle during travel using a combination of data received from two or more of the sensors.
 7. The system of claim 1 wherein the data processing module is further configured to verify, before generating the second indication, whether the item has moved within the storage area or has fallen from the vehicle using a combination of data received from two or more of the sensors.
 8. The system of claim 1 wherein the sensors include two or more of a camera, a weight sensor, a suspension sensor, a radar sensor, an ultrasonic sensor, and a tire pressure sensor.
 9. The system of claim 1 wherein the storage area includes at least one of a trunk of the vehicle, a cargo area of the vehicle, a rack mounted on a roof of the vehicle, and a trailer hitched to the vehicle.
 10. The system of claim 1 wherein the data processing module comprises a neural network to generate the first indication, the neural network being trained using machine learning to determine when the item is likely to move within the storage area or is likely to fall from the vehicle during travel based on the data from the sensors and additional data regarding at least one of road design, road conditions, and speed limit of a route selected for travel, and historical driving behavior of a driver of the vehicle.
 11. A system comprising: a processor; and a memory comprising instructions for execution by the processor to: process data from a plurality of sensors arranged in a vehicle to monitor an item in a storage area of a vehicle; determine whether the item in the storage area of the vehicle is likely to move within the storage area or is likely to fall from the vehicle during travel; generate a first indication that the item is likely to move within the storage area or is likely to fall from the vehicle during travel; determine whether the item has moved within the storage area or has fallen from the vehicle during travel; and generate a second indication that the item has moved within the storage area or has fallen from the vehicle during travel.
 12. The system of claim 11 wherein the instructions further cause the processor to include in the first indication a suggestion regarding one or more of repositioning the item in the storage area, a route to a location to pull over for repositioning the item in the storage area, an alternate route for the travel, and a change in driving behavior.
 13. The system of claim 11 wherein the instructions further cause the processor to adjust suspension of the vehicle in response to determining that the item is likely to move within the storage area or is likely to fall from the vehicle during travel.
 14. The system of claim 11 wherein the instructions further cause the processor to include in the second indication an identification of the item, a location where the item fell from the vehicle, and a route to the location in response to determining that the item fell from the vehicle during travel.
 15. The system of claim 11 wherein the instructions further cause the processor to generate a message to send to another vehicle including a location where the item fell from the vehicle in response to determining that the item fell from the vehicle during travel.
 16. The system of claim 11 wherein the instructions further cause the processor to verify, before generating the first indication, whether the item is likely to move within the storage area or is likely to fall from the vehicle during travel using a combination of data received from two or more of the sensors.
 17. The system of claim 11 wherein the instructions further cause the processor to verify, before generating the second indication, whether the item has moved within the storage area or has fallen from the vehicle using a combination of data received from two or more of the sensors.
 18. The system of claim 11 wherein the sensors include two or more of a camera, a weight sensor, a suspension sensor, a radar sensor, an ultrasonic sensor, and a tire pressure sensor.
 19. The system of claim 11 wherein the storage area includes at least one of a trunk of the vehicle, a cargo area of the vehicle, a rack mounted on a roof of the vehicle, and a trailer hitched to the vehicle.
 20. The system of claim 11 further comprising a neural network configured to generate the first indication, the neural network being trained using machine learning to determine when the item is likely to move within the storage area or is likely to fall from the vehicle during travel based on the data from the sensors and additional data regarding at least one of road design, road conditions, and speed limit of a route selected for travel, and historical driving behavior of a driver of the vehicle. 